Evaluating the dendroclimatological potential of blue intensity on multiple conifer species from Australasia

We evaluate a range of blue intensity (BI) tree-ring parameters in eight conifer species (12 sites) from Tasmania 15 and New Zealand for their dendroclimatic potential, and as surrogate wood anatomical proxies. Using a dataset of ca. 10-15 trees per site, we measured earlywood maximum blue reflectance intensity (EWB), latewood minimum blue reflectance intensity (LWB) and the associated delta blue intensity (DB) parameter for dendrochronological analysis. No resin extraction was performed, impacting low frequency trends. Therefore, we focused only on the high frequency signal by detrending all tree-ring and climate data using a 20-year cubic smoothing spline. All BI parameters express low relative variance and weak 20 signal strength compared to ring-width. Correlation analysis and principal component regression experiments identified a weak and variable climate response for most ring-width chronologies. However, for most sites, the EWB data, despite weak signal strength, expressed strong calibrations with summer temperatures. Significant correlations for LWB were also noted, but the sign of the relationship for most species is opposite to that reported for all conifer species in the Northern Hemisphere. DB performed well for the Tasmanian sites but explained minimal temperature variance in New Zealand. Using 25 the full multi-species/parameter network, excellent summer temperature calibration was identified for both Tasmania and New Zealand ranging from 52% to 78% explained variance, with equally robust independent validation (Coefficient of Efficiency = 0.41 to 0.77). Comparison of the Tasmanian BI reconstruction with a wood anatomical reconstruction shows that these parameters record essentially the same strong high frequency summer temperature signal. Despite these excellent results, a substantial challenge exists with the capture of potential secular scale climate trends. Although DB, band-pass and 30 other signal processing methods may help with this issue, substantially more experimentation is needed in conjunction with comparative analysis with ring density and quantitative WA measurements. https://doi.org/10.5194/bg-2021-119 Preprint. Discussion started: 21 May 2021 c © Author(s) 2021. CC BY 4.0 License.


Introduction
The range of variables that are now routinely measured from the rings of trees, including width, stable isotopes, multiple wood anatomical features and density, has increased substantially in recent years (McCarroll et al. 2002;McCarroll and 35 Loader, 2004;Drew et al. 2013;von Arx et al. 2016;Björklund et al., 2020). However, our knowledge of the climatic, environmental, and physiological processes that modulate the year-to-year variability of these different tree-ring parameters is still far from comprehensive. Since the early seminal work of Fritts et al. (1965), a well-known rule of thumb for ringwidth (RW) based dendroclimatology is that trees sampled near their high elevation or latitude treelines will be predominantly temperature limited, while at lower elevations or latitudes, moisture limitation becomes the primary driver of 40 growth (Fritts 1976;Kienast et al. 1987;Buckley et al. 1997;Wilson and Hopfmüller 2001;Briffa et al., 2002;Babst et al. 2013;St. George 2014). Such targeted sampling is strategically vital in "traditional" dendroclimatology and robust reconstructions can be derived so long as tree-line sites are sampled where a single dominant climate parameter controls growth (Bradley 1999). However, the climatic influence on RW can be complex and there are many published studies where the relationship between RW and climate is shown to be temporally unstable and/or non-linear (Wilmking et al. 2020) or is 45 unexpected but consistent between many sites (Cook and Pederson 2011).
Ring density parameters, especially maximum latewood density (MXD), have been shown to provide substantially more robust estimates of past summer temperature compared to RW (Briffa et al., 2002;Wilson and Luckman, 2003;Esper et al., 2012;Büntgen et al., 2017;Ljungqvist et al., 2020). Density data may also retain a strong temperature signal at elevations 50 below the upper treeline, minimising the non-linear influence of a changing tree-line elevation through time (Kienast et al. 1987). The use of ring-density variables from lower elevation or latitude sites to reconstruct past hydroclimate is rare (Camarero et al. 2014(Camarero et al. , 2017Cleaveland 1986;Seftigen et al. 2020) and is clearly an area demanding further attention.
The reconstructive value of tree ring stable isotopes (carbon and oxygen) appears to be less constrained to climate limited 55 locations and substantial potential exists from mid-latitude regions where traditional dendroclimatological approaches are less reliable (McCarroll and Loader, 2004;Loader et al. 2008;Young et al. 2015;Loader et al. 2020;Büntgen et al. 2021).
However, within the mechanistic framework of stable isotopes, there is still much to explore regarding the complex associations between fractionation and climate for different species and across different ecotones.
Despite the strong climate signal often noted in such non-RW tree-ring parameters, their procurement is expensive, often requires specialised equipment, and is time consuming. Consequently, there are substantially less published data available for inspection. In recent years, blue intensity (BI) has been championed by many groups as a cheaper surrogate for maximum 70 latewood density (Björklund et al., 2014(Björklund et al., , 2015Rydval et al., 2014;Wilson et al., 2014). In its common usage, BI measures the intensity of the reflectance of blue light from the latewood of scanned conifer samples so that a dense (dark) latewood would result in low reflectance values. MXD and BI essentially measure similar wood properties. Most studies that have directly compared MXD and latewood BI show no significant difference in the climate response of the two parameters Björklund et al 2019;Ljungqvist et al., 2020;Reid and Wilson 2020). Though the acceptance of BI in 75 dendrochronology was initially slow after publication of the original concept paper (McCarroll et al. 2002), over the past decade many BI-based studies have been published. These studies have examined the use of BI as an ecological and climatological indicator in a variety of conifer species from several locations around the Northern Hemisphere (Campbell et al., 2007(Campbell et al., , 2011Helama et al., 2013;Rydval et al., 2014Rydval et al., , 2017Rydval et al., , 2018Björklund et al., 2014Björklund et al., , 2015Wilson et al., 2014Wilson et al., , 2017aWilson et al., , 2017bWilson et al., , 2019Babst et al., 2016;Dolgova, 2016;Arbellay et al., 2018;Buras et al., 2018;Fuentes et al., 2018;Kaczka 80 et al., 2018;Wiles et al., 2019;Harley et al. 2020;Heeter et al. 2020;Reid and Wilson 2020).
Only three studies that utilise BI data south of 30oN have been published. Buckley et al. (2018) explored the potential of reflectance parameters from the tropical conifer Fujian cypress (Fokienia hodginsii) from central Vietnam and found a significant positive relationship between earlywood maximum BI and December-April maximum temperature. Although a 85 spring/early summer temperature signal is extant in Northern Hemisphere conifer minimum density data , correlations are generally not as strong as the earlywood results detailed by Buckley et al. (2018). In the Southern Hemisphere, Brookhouse and Graham (2016) measured latewood BI from Errinundra plum-pine (Podocarpus lawrencei) samples taken from the Australian Alps and identified a strong inverse (r = -0.79) relationship with August-April maximum temperatures, suggesting substantial potential for this species if long-lived specimens could be found. Finally, Blake et al. 90 (2020) recently explored the climate signal in BI parameters measured from Silver pine (Manoao colensoi) samples growing on the New Zealand's South Island and found strong significant relationships between both earlywood and latewood BI parameters and summer temperatures. Although the sign (positive) of the earlywood BI relationship with temperature agreed with results detailed in other studies Buckley et al. 2018), the latewood relationship was inverse to that detailed for Northern Hemisphere conifers (Briffa et al. 2002) and observed by Brookhouse and Graham (2016 Here we expand upon the pilot studies of Brookhouse and Graham (2016) and Blake et al. (2020) and explore the climate signal of earlywood and latewood BI from several key conifer species from Tasmania and New Zealand. To minimise 100 nomenclature confusion, we refer to the reflectance parameters as earlywood blue intensity (EWB) and latewood blue intensity (LWB). Based on ecophysiological theory  we posit that EWB, derived from maximum intensity values of the whole-ring reflectance spectrum, essentially provides a surrogate for mean lumen size of the earlywood cells, while LWB, derived from minimum reflectance values, reflects the relative density of the darker latewood cell walls. We further suggest these reflectance measures are useful surrogate measures of mean tracheid diameter and cell 105 wall thickness, which are proven to be excellent proxies of past climate (Allen et al. 2018;Björklund et al. 2019) but are laborious and expensive to measure directly. As well as undertaking a dendroclimatic assessment of multiple BI parameters in different Australasian, temperature sensitive confers, we also assess the potential for furthering the analysis of wood anatomical parameters in Australasian dendroclimatology. Improving terrestrial based estimates of past temperature in the land-limited Southern Hemisphere (Neukom et al. 2014) will only be achieved by enhancing the strength of the calibrated 110 signal that until recently has been characterized solely by low performing ring-width data. Table 1: Chronology information for the seven Tasmanian and 5 New Zealand sites used in the study (see Figure 1).

2 Data and Methods
Four tree species from Tasmania and New Zealand were targeted for analysis ( Figure 1, Table 1) representing species that have not only been used in previous dendrochronological studies, but each has the potential to produce climate proxy records more than 1000 years in length. Until recently, RW data were used for most Australasian dendroclimatological studies but 120 calibration results never exceeded 40-45% explained variance (Allen et al. 2018). In Tasmania, the strongest calibration results for summer temperatures had been obtained using high elevation Huon pine (Lagarostrobos franklinii - Buckley et al. 1997;Cook et al. 2006) although some coherence was also found for Pencil pine (Athrotaxis cupressoides) and King Billy however, express a complex non-linear relationship with climate and has not been used for dendroclimatic reconstruction 125 (Allen et al. 2001). By contrast summer temperature calibration experiments performed on measurement series of several wood anatomical properties from these same species have shown substantial improvements over RW (Allen et al. 2018), as well as the development of hydroclimate reconstructions (Allen et al. 2015a/b). In New Zealand, RW based summer temperature reconstructions have been developed from NZ Cedar (Libocedrus bidwillii -Palmer and Xiong 2004), Silver pine (Manoao colensoi - Cook et al. 2002Cook et al. , 2006 and Pink pine (Halocarpus biformis -D' Arrigo et al. 1996, Duncan et al. 130 2010) although ring density (Xiong et al. 1998 -Pink pine) and BI (Blake et al 2020 -Silver pine) measured from earlywood cells have produced stronger results. Kauri (Agathis australis) is the longest-lived tree species in Australasia (Boswijk et al. 2014) and is notable in that it expresses a strong stable relationship with indices of the El Nino Southern Oscillation (Cook et al. 2006;Fowler et al. 2012).  Table 1). Also indicated (grey boxes) are the regional domains of the gridded CRU TS 4.03 temperature and precipitation data ( In this study, we utilised tree cores sampled over the past three decades and prepared for RW measurement. Considering the focus of this study is to assess the potential of BI parameters for enhancing dendroclimatic reconstruction, and the fact that 145 the samples were already mounted, no resin extraction was performed except for the Silver pine AHA site (see Blake et al. 2020 for details). As many of the species are resinous by nature, this immediately imposes a potential problem for measuring reflectance data, because any inhomogeneous resin-related discolouration will impact intensity values Björklund et al., 2014Björklund et al., , 2015Wilson et al. 2017b;Reid and Wilson 2020). Consequently, as the high frequency signal will only be minimally affected by discolouration (Wilson et al. 2017a), all analyses for this proof-of-concept study will utilise 150 only the high pass fraction of the chronologies.
The mounted samples were re-sanded using fine grade (> 600 grit) sandpaper to remove decadal markings. Samples were scanned at multiple institutions using different scanners and a range of resolutions from 1200 to 3200 DPI. RW and BI data were generated using CooRecorder (Cybis 2016, http://www.cybis.se/forfun/dendro/index.htm) except for AHA 155 (WinDendro -see Blake et al. 2020). Despite many of the samples being substantially older, most samples were measured only back into the 17 th or 18 th centuries (with site MHP (Table 1) being an exception), providing enough data to ensure robust calibration and validation over the instrumental period and to allow comparison with a temperature reconstruction from Tasmania based on wood anatomy (Allen et al. 2018). Parameters generated for analysis were RW, EWB and LWB.
The LWB data were not inverted as is the norm in Northern Hemisphere studies using data generated in CooRecorder 160 ).
Perhaps the greatest limitation for light reflectance-based data is that any colour changes that do not represent year-to-year changes in wood anatomical features such as lumen size and cell wall thickness will impose a colour-related bias in the intensity measurements. Examples of non-anatomically related colour changes are those associated with the 165 heartwood/sapwood transition, sections of highly resinous wood, or fungal staining. Björklund et al. (2014) proposed a potential procedure that could correct for such colour changes. This procedure subtracts the LWB reflectance value from the EWB data producing a delta parameter (hereafter referred to as Delta BI -DB). Theoretically, DB should correct for common colour change biases between heartwood and sapwood and even resinous zones within the wood. To date, DB has been utilised successfully in only a few studies (Björklund et al., 2014(Björklund et al., , 2015Wilson et al., 2017b;Fuentes et al. 2018;Blake 170 et al. 2020;Reid and Wilson 2020). As no resin extraction was performed (except site AHA, Table 1) and all the species used for this study express a colour change from heartwood to sapwood, DB data will also be examined to explore its dendroclimatic potential. years respectively. This is not surprising given that the smoothing spline, operating as a symmetric digital filter, is not well suited for dealing with abrupt changes in time series such as that observed in the CMewb chronology. In fact, the bias of low 180 (pre-transition) and high (post-transition) index values are only minimised when a flexible 20-year spline is used because it better adapts to the observed discontinuity. However, this adaptability comes at the cost of losing potentially valuable >20year variability in the time series. This is clearly undesirable and better ways of modelling and removing such discontinuities without the unwanted loss of lower-frequency variability are needed. Although less flexible splines could be used for other species with a slower or minimal colour change from heartwood to sapwood ( Figure A1), a consistent approach to 185 detrending was deemed prudent and therefore a 20-year spline was used for all datasets.
The mean interseries correlation statistic (RBAR) is utilised to assess how many series are needed to attain an Expressed Population Signal value of 0.85 (Wigley et al. 1984;Wilson and Elling 2004). Previous research has shown that the common signal expressed by BI data can be rather weak (Wilson et al. , 2017a(Wilson et al. /b, 2019Kaczka et al. 2018;Wiles et al. 2019). 190 We explore this phenomenon further with this multi-parameter/species network by using the coefficient of variation to help understand relative internal variance and co-variance of the parameter chronologies.
The climate signal expressed in the individual chronologies was initially explored using simple correlation analysis against monthly gridded (see Figure 1 for locations) CRUTS 4.03 temperature and precipitation data (Harris et al. 2014(Harris et al. ) for the 195 periods 1902(Harris et al. -1995(Harris et al. , 1902(Harris et al. -1950(Harris et al. and 1951(Harris et al. -1995(Harris et al. . 1902 was the initial start year as correlations were performed over 20 months including the previous growing season while 1995 reflects the final common year for all tree-ring datasets (Table 1).
Correlations with monthly precipitation were weak, variable and temporally unstable for all species/parameter chronologies and the results are presented in the Appendix but not discussed further (see Figure A4a-d).

200
Principal component analysis (PCA) was used on varying subsets of chronologies for each region (i.e. all chronologies of the same parameter, or all parameters from a single species) to reduce the data to a few modes of common variance. Principal components that had both an eigenvalue > 1.0 and correlated significantly (95% C.L.) with the target instrumental data were entered into a stepwise multiple regression and calibrated against a range of seasonal temperatures. For New Zealand, the three CRU TS 4.03 grid boxes ( Figure 1) were averaged to create a country wide mean series. This was justified as the three 205 inter-grid box mean correlation values between all tested seasons was 0.93 (STDEV = 0.01) suggesting there is a strong common signal between North Island and southern South Island. PCA was also utilised to ascertain the optimal season for dendroclimatic calibration using the full chronology network for each country as well as exploring seasonal differences between parameters and species. Analyses were performed over the common period of all tree-ring and climate data  as well as early  and late  period calibration and verification. The Coefficient of Efficiency 210 (CE -Cook et al., 1994) was used to validate the regression-based climate estimates.  Figure 2b). Therefore, following traditional methodologies to assess signal strength, more BI series are needed than RW to attain a robust chronology. On average, to attain an EPS value of at least 0.85 (Wigley et al. 1984), 14 series would be needed for RW, while 44, 47 and 58 series would be needed for EWB, LWB and DB respectively.
This weaker common signal of the BI parameters has been noted before (Wilson et al. , 2017a(Wilson et al. /b, 2019Kaczka et al. 2018;Wiles et al. 2019;Blake et al. 2020) and is also noted in wood anatomical data from Tasmania (Allen et al. in prep). 225 The common signal is particularly weak for Celery Top and Kauri (EWB) and Pink pine and Kauri (LWB and DB -see Table A1 for detailed values). A scatter plot of the CV and RBAR data ( Figure 2c) suggests that the common signal expressed by these chronologies is partly a function of the relative variance of the time-series (r = 0.72, p < 0.001). Although the range in RBAR values for the EWB and LWB data suggests some uncertainty in this observation (see also Table A1), the implication of these results is that the relatively low variation of values around the mean for the BI parameters suggests that any anomalous colour staining on the wood that does not reflect the true wood properties being measured could have a large impact on the chronology 240 common signal. However, it should be emphasised that a weak common signal and low EPS value does not necessarily result in a weak climate signal (Buras 2017).

Climate response
The strength of correlations between the RW chronologies and mean monthly temperatures varies across species. Over the full period ( The DB chronologies express a range of responses to temperature that are all generally weaker than for EWB and LWB ( Figure 3). Significant positive correlations with summer temperatures are found for RCS, MMWTRL, MHP, and DPP. HUP and AHA also express some weak positive summer temperature coherence. Negative correlations are noted for CM, BUT, 280 PKL and FLC. However, many of these correlations are not temporally stable when compared over the 1902-1950 and 1951-1995 periods (Figure A3d). Current theory suggests that DB should perform well when EWB and LWB parameters are weakly correlated and express different earlier and later seasonal climate response (Björklund et al., 2014). However, the results herein indicate that this simple hypothesis does not consistently apply in this multi-species study. For example, the EWB and LWB data for the Pink pine DPP site express different early and late seasonal responses with temperatures ( Figure  285 3), but still show a reasonably high inter-parameter correlation (0.60, Table A2). However, the DB data still expresses a significant and strong response with summer temperatures, although marginally weaker than the EWB response. On the other hand, DB for the Pencil pine sites (MCK and CM) behaves more like conifers in the Northern Hemisphere (Björklund et al., 2014;Wilson et al. 2017b), with significant correlations noted for both EWB and LWB with summer temperatures, but, likely due to the high inter-parameter correlation (0.57 and 0.68), the DB data express weak, or even inverse correlations 290 with summer temperatures. Overall, the DB results are mixed and disappointing. This parameter theoretically could minimise the colour bias of the darker to lighter colour heartwood/sapwood transition ( Figure A1) but, for the data used herein, this appears not to be the case. These results suggest that alternative approaches to using DB may need to be explored to minimise the impact of the heartwood/sapwood change noted in most of the species used in this study.

Parameter and species-specific principal component calibration tests 295
The previous section detailed that temperature is the predominant climate signal expressed across species and parameters in these chronologies from Tasmania and New Zealand (Figures 3, A3a-d). Only weak coherence with precipitation was found ( Figure A4a-d). To further explore the climate response, principal component regression calibration  experiments with seasonal temperature were performed to ascertain which combination of BI parameters and species express the strongest climate signal and therefore should be the focus for future research -including refined BI measurement and/or 300 wood anatomical measurement.
For Tasmania, the PCA identifies 3, 2, 2 and 2 significant principal components for RW, EWB, LWB and DB respectively.
Each BI parameter PC regression explains > 40% of the temperature variance while RW is substantially poorer at 21% (Figure 4a). Both EWB and LWB explain 43% of the December-February and January-March variance respectively -these 305 seasons being biologically logical with respect to the earlier seasonal start for EWB and later end for LWB. Despite the sitespecific DB data correlating with temperature more weakly than EWB and LWB (Figure 3), their multivariate combination calibrates better (48%) with January-March temperatures. Although this is an encouraging result as DB may theoretically https://doi.org/10.5194/bg-2021-119 Preprint. Discussion started: 21 May 2021 c Author(s) 2021. CC BY 4.0 License. correct for colour related biases, the mix of positive and negative zero order correlations with temperature ( Figure 3) suggest that some caution will be needed if such data are used to capture more secular scale information. 310

315
For New Zealand, PCA identifies 3, 2, 2 and 3 significant principal components for RW, EWB, LWB and DB respectively.

Tasmania New Zealand
Of all the species tested, Tasmanian Pencil pine returns the strongest calibration (47%) with January-February temperatures ( Figure 4b) although New Zealand Silver pine and Pink pine also calibrate reasonably with 41% (December-January) and 42% (September-January). It should be noted that two Pencil pine sites were used (Table 1) compared to only one each for Silver pine and Pink pine which likely will influence these results. Of the species models (Figure 4b), King Billy pine, Huon 325 pine and Kauri explain 30% (December-February), 34% (January-February) and 33% (November-April) respectively of the temperature variance with New Zealand cedar still showing some reasonable coherence (26%) for December-March. Celery Top is the weakest species explaining only 10% of the December-January temperature variance.

Region wide calibration and validation
A multi-site, multi-species approach to dendroclimatology can improve overall calibration even if some of the sampled sites 330 and species are not located close to climate limited treeline ecotones (Alexander et al. 2019). Herein we have an opportunity to pool all the data for each country to create a combined multi-species and multi-parameter reconstruction. As the optimal season for calibration varies as a function of species and parameter (Figure 4), initial PC regression experiments using all chronologies from each of the two regions was performed. For each of these models, all PCs with an eigenvalue > 1.0 were entered into the regression model. January-February (JF) temperature was identified as the overall optimal season for 335 Tasmania while December-January (DJ) provided the strongest calibration for New Zealand. Forcing all variables into the PC regression model also provides an opportunity to identify the importance of each species parameter towards the development of regional reconstructions. The beta weights (Cook et al. 1994) from the regression modelling (Table 2) clearly show the strong influence of the EWB parameters in the multiple regression model, especially from Pencil pine (MCK and CM) and Silver pine (AHA) although strong beta weights are also noted for King Billy pine (MDR), Huon pine 340 (MHP) and Pink pine (DPP). Other parameters that provide useful information in the modelling are RW (King Billy pine (MWWTRL) and Huon pine (MHP)), LWB (Pencil pine (MCK, CM), Huon pine (BUT) and Kauri (PKL)) and DB (Huon pine (MHP) and Pink pine DPP)). These results are consistent with the correlation response function analysis (Figure 3), but it must be emphasised that the results shown in Table 2     beta weights using all parameter and species data. The Tasmanian modelling was against January-February temperatures while New Zealand was with December-January.

350
For the final countrywide calibration and validation experiments, three PC regression approaches were used, each reflecting more stringent screening procedures; (1) as already detailed above -all data entered into PCA and PCs with an eigenvalue > 1.0 that correlated significantly (95%) with the instrumental target were entered as possible candidates into a stepwise 355 multiple regression; (2) same as (1) but chronologies were initially screened for significant correlation with the full period instrumental target before PCA; (3) similar to previous variants, but significant correlations between the chronologies and the instrumental target for both the 1901-1950 and 1951-1995 periods were required. For Tasmania, the initial 28 parameter chronologies were reduced to 17 and 10 respectively via the two more stringent 360 screening procedures while the 20 initial chronologies from New Zealand were reduced to 13 and 7 respectively ( Figure 5).
Full period    Overall, the temperature reconstruction experiments for both Tasmania and New Zealand ( Figure 5) return excellent results 385 with overall calibration ar2 values well in excess of 0.60. Although no wood anatomical data exists yet for New Zealand, Allen et al. (2018) recently produced a range of PC regression based Tasmanian summer temperature reconstructions from a network of 58 chronologies using RW, mean tracheid radial diameter, mean cell wall thickness, mean density and microfibril angle. These variables were measured using the SilviScan system (Evans, 1994) from the same four Tasmanian tree species used herein. Strong calibration results explaining 50-60% of the temperature variance and robust validation were also noted 390 in their analyses. We compare our full period screened temperature reconstruction (Variant 2, Figure 5) Figure A1). Overall, the BI data, at least for Tasmania, basically express the same high frequency signal as the WA data used in Allen et al. (2018) and the results herein suggest that BI parameters could provide excellent proxies of past growing season temperatures. However, for their potential to be truly realised, the heartwood/sapwood colour bias needs to be overcome. 410

Conclusions and future research directions
In this study, we measured a range of blue intensity parameters from eight conifer species from Tasmania and New Zealand to ascertain whether the use of EWB, LWB and/or DB can improve upon previous RW-only based dendroclimatic reconstructions that explain about 40-45% of the temperature variance. No attempt to remove resins was made for this proofof-concept study. Therefore, due to the impact on reflectance-based parameters of resins and heartwood/sapwood colour 415 https://doi.org/10.5194/bg-2021-119 Preprint. Discussion started: 21 May 2021 c Author(s) 2021. CC BY 4.0 License. changes on the wood, we detrended the chronologies and climate data using a very flexible spline (20-years) to focus only on the high frequency signal. Metrics denoting signal strength (RBAR and EPS) indicated a very weak common signal in the BI parameters (mean RBAR range 0.14 -0.16, Figure 2b) compared to the RW data (mean RBAR = 0.33) which appeared to be partly related to the relative variance in these datasets. The EWB data in particular exhibit very low variability which may mean that any colour variation in the wood that does not reflect true year-to-year wood anatomical variance may have a large 420 impact on such data, thus weakening the common signal.
Despite the weak common signal expressed by the BI parameters, the climate signal extant in these data is very strong, especially EWB. When all parameters are combined using PC regression, depending on the period used, 52-78% of the summer temperature variance can be explained ( Figure 5). This is generally greater than the norm for Northern Hemisphere 425 based MXD/BI related temperature reconstructions (Wilson et al. 2016), although admittedly, the results in this study are focussed only on the high frequency fraction of the data. These strong calibration results are driven mainly by EWB data from Pencil pine, Huon pine and King Billy pine (Tasmania) and Silver pine, Pink pine and cedar (New Zealand) although useful information was also identified in LWB (Pencil pine, low elevation Huon pine, Kauri and cedar), DB (high elevation Huon pine and Pink pine) and RW (high elevation Huon pine - Table 2). However, the relationship of LWB for most species 430 with summer temperatures is opposite in sign to that observed in the Northern Hemisphere and further study is needed to assess the physiological processes leading to this inverse relationship in these particular Southern Hemisphere conifers.
The similarity of the Tasmanian multi-TR-proxy reconstruction with a reconstruction heavily dependent on wood anatomical data (Allen et al. (2018) - Figure 6) clearly highlights that the BI and WA data express similar wood properties. This is a 435 highly encouraging result for the utilisation of BI as it is quicker and cheaper to produce than WA data. However, the "elephant in the room" is whether robust low frequency information can be extracted from BI based parameters or is it an analytical methodology that ultimately will be relevant only for decadal and higher frequencies. It is unlikely that the heartwood/sapwood colour change (both sharp and gradual - Figure A1), expressed by most of the tree species used in this study, can be fully removed by resin extraction alone. Some success at overcoming heartwood/sapwood colour bias using 440 DB has been shown for some Northern Hemisphere conifer species (Björklund et al., 2014(Björklund et al., , 2015Wilson et al., 2017b;Fuentes et al. 2018;Reid and Wilson 2020), but the DB results detailed herein ( Other statistical approaches have been used to overcome the colour bias using either contrast adjustments (Björklund et al., 445 2015;Fuentes et al., 2018) or band-pass approaches where the low frequency signal is derived from the RW data and the high frequency is driven by the BI data (Rydval et al., 2017) but further experimentation is needed. We hypothesise that relatively sharp changes in colour intensity measures related to the heartwood/sapwood transition can be viewed conceptually in a similar way to how endogenous disturbances affect ring-width parameters over time (Cook 1987 to the progress in developing growth release detection methods to reconstruct canopy disturbance histories of forests 450 (Altman 2020, radial growth averaging (Lorimer and Frelich 1989) or time series methods (Druckenbrod et al., 2013;Rydval et al. 2016) could be used to identify and remove the colour bias signature resulting from the change in physiology from heartwood to sapwood. However, to facilitate such signal processing methods, more studies are needed to directly compare both MXD and WA with BI data to understand the secular trend biases in these light reflectance parameters. At the very least, the results detailed herein clearly show that BI parameters can be used to identify 455 those species that should be targeted for more costly and time-consuming analytical methods such as wood anatomical measurements.   Allen, K.J., Lee, G., Ling, F., Allie, S., Willis, M. and Baker, P.J., 2015a. Palaeohydrology in climatological context: developing the case for use of remote predictors in Australian streamflow reconstructions. Applied Geography, 64, pp.132-152.